In this work we examine the effect of dialog acts on word use, in context of the influence of interlocutors in a polylogue on each other. The basic idea of this work is the extension of the cache model and the influence model by dialog act information. The cache model covers the re-usage of words and the influence model calculates the influence of interlocutors in a polylogue on each other. Both approaches could be used to improve the word prediction accuracy in a word generative model. We start to examine the usage of dialog acts to improve our word generative model in terms of perplexity. For the usage of dialog acts, a knowledge about the future dialog act is required. Therefore, we examine how dialog act miss-prediction influences the resulting performance. Further on, we introduce a new approach to generate artificial dialog acts which guarantees the knowledge about the following dialog act. Our final experiments present the improvements in terms of perplexity using our new approach in AMI, NIST and NTT meeting corpora.